摘要： 标准势均衡多目标多伯努利算法在低检测率下对目标数目估计过少。针对该问题,提出一种改进的势均衡多目标多伯努利算法。通过在上一时刻滤波过程中对某些特定的高斯项进行修正和保存,将修正后的高斯项合并到更新后的高斯项中,用到下一时刻的滤波步骤中,以削减低检测率带来的影响。仿真实验结果表明,在低检测率下,改进算法能够在一定程度上解决目标数目估计过少的问题,提高算法的目标跟踪精度。

关键词: 势均衡多目标多伯努利算法, 低检测率, 目标跟踪, 状态估计, 随机有限集

Abstract: The number of targets is under-estimated in the standard Cardinality Balanced Multi-target Multi-Bernoulli(CBMeMBer) algorithm when the detection rate is low.To address this problem,an improved CBMeMBer algorithm is proposed.In the previous filtering process,some particular Gaussian items are modified and saved.And these items are merged into the updated Gaussian items and used in the next filtering circle.As a result,the negative effect of low detection rate is reduced.Simulation results show that the improved algorithm can solve the problem of under-estimated target in some extent and improve the accuracy of target tracking under low detection rate.

Key words: Cardinality Balanced Multi-target Multi-Bernoulli(CBMeMBer) algorithm, low detection rate, target tracking, state estimation, random finite set

中图分类号: 

• TP391